{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8256301f",
   "metadata": {},
   "source": [
    "# 卷积神经网络\n",
    "\n",
    "## 神经元\n",
    "$ y=f(x_1 \\cdot \\omega_1 + \\cdots + x_n \\cdot \\omega_n - \\theta) $\n",
    "\n",
    "## BP神经网络\n",
    "BP算法包括信号的向前传播和误差的反向传播\n",
    "\n",
    "## 卷积层\n",
    "目的：进行特征识别\n",
    "\n",
    "实质：滑动窗口\n",
    "\n",
    "特点：\n",
    "    \\begin{itemize}\n",
    "        \\item 局部感知\n",
    "        \\item 权值共享\n",
    "    \\end{itemize}\n",
    "\n",
    "\\begin{itemize}\n",
    "    \\item 卷积核的channel和输入特征的channel数量相同 （例如RGB输入的图像 卷积核的channel就是3）\n",
    "    \\item 输出的特征矩阵channel与卷积核个数相同\n",
    "\\end{itemize}\n",
    "\n",
    "加bias可以直接广播到矩阵上\n",
    "\n",
    "在卷积操作时，矩阵卷积后尺寸由以下因素决定：\n",
    "\\begin{itemize}\n",
    "    \\item 输入大小 WxW\n",
    "    \\item Filter大小FxF\n",
    "    \\item 步长 S\n",
    "    \\item padding像素数\n",
    "\\end{itemize}\n",
    "N = (W - F + 2P)/S+1\n",
    "\n",
    "\n",
    "## 激活函数\n",
    "引入非线性因素\n",
    "\n",
    "### Sigmod \n",
    "$ f(x) = \\frac{1}{1+e^{-x}}$ \n",
    "\n",
    "Sigmod激活函数饱和时梯度很小，网络层数深时易出现梯度消失\n",
    "\n",
    "### ReLu\n",
    "$ f(x) = Max(0,x) $ \n",
    "\n",
    "反向传播过程中有一个非常大的梯度经过时，反向传播可能使权值分布中心小于0,反向传播无法更新权值\n",
    "\n",
    "\n",
    "## 池化层\n",
    "\n",
    "### MaxPooling 下采样\n",
    "对NxN区域做去最大值操作\n",
    "\n",
    "### AveragePooling 下采样\n",
    "对NxN区域做去平均值操作\n",
    "\n",
    "### 特点\n",
    "\\begin{itemize}\n",
    "    \\item 没有训练参数\n",
    "    \\item 只改变矩阵的w和h\n",
    "    \\item 一般pool size 和 stride 相同\n",
    "\\end{itemize}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e3d1f1",
   "metadata": {},
   "source": [
    "# 反向传播过程的补充\n",
    "以下图为例\n",
    "<img width=\"50%\" src=\"\"></img>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3dbdff0",
   "metadata": {},
   "source": [
    "\\begin{align}\n",
    "    y_1 &= w_{11}^{(2)} \\cdot \\sigma(x_1 \\cdot w_{11}^{(1)} + x_2 \\cdot w_{21}^{(1)} + b_1^{(1)}) + \\nonumber \\\\\n",
    "        &= w_{21}^{(2)} \\cdot \\sigma(x_1 \\cdot w_{12}^{(1)} + x_2 \\cdot w_{22}^{(1)} + b_2^{(1)}) + \\nonumber \\\\\n",
    "        &= w_{31}^{(2)} \\cdot \\sigma(x_1 \\cdot w_{13}^{(1)} + x_2 \\cdot w_{23}^{(1)} + b_3^{(1)}) + b_1^{(2)} \\nonumber \\\\ \\nonumber \\\\\n",
    "    y_2 &= w_{12}^{(2)} \\cdot \\sigma(x_1 \\cdot w_{11}^{(1)} + x_2 \\cdot w_{21}^{(1)} + b_1^{(1)}) + \\nonumber \\\\\n",
    "        &= w_{22}^{(2)} \\cdot \\sigma(x_1 \\cdot w_{12}^{(1)} + x_2 \\cdot w_{22}^{(1)} + b_2^{(1)}) + \\nonumber \\\\\n",
    "        &= w_{32}^{(2)} \\cdot \\sigma(x_1 \\cdot w_{13}^{(1)} + x_2 \\cdot w_{23}^{(1)} + b_3^{(1)}) + b_2^{(2)} \\nonumber\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8f9ce81",
   "metadata": {},
   "source": [
    "## Softmax 激活函数\n",
    "\n",
    "$ o_1 = \\frac{e^{y_1}}{e^{y_1}+e^{y_2}} $\n",
    "\n",
    "$ o_2 = \\frac{e^{y_2}}{e^{y_1}+e^{y_2}} $"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4aff283",
   "metadata": {},
   "source": [
    "## Cross Entropy Loss 交叉熵损失\n",
    "\n",
    "1. 多分类输出\n",
    "$ H = -\\sum\\limits_i^N{o_i^*log(o_i)} $\n",
    "2. 二分类输出\n",
    "$ H = -\\frac{1}{N}\\sum\\limits_{i=1}^{N}{\\left[o_i^*log(o_i) + (1-o_i^*)log(1-o_i)\\right]} $   \n",
    "$o_i^*$为真实值 $o_i$为预测值\n",
    "\n",
    "本例使用softmax 故损失函数为 $ Loss = -(o_1^*log(o_1)+o_2^*log(o_2) $\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d817abd",
   "metadata": {},
   "source": [
    "## 误差的反向传播\n",
    "\n",
    "以 $w_{11}^{(2)}$为例\n",
    "\\begin{align}\n",
    "    \\frac{\\partial Loss}{\\partial w_{11}^{(2)}} \n",
    "    &= \\frac{\\partial Loss}{\\partial y_1} \\cdot \\frac{\\partial y_1}{\\partial w_{11}^{(2)}} \\nonumber \\\\\n",
    "    &= \\left( \n",
    "            \\frac{\\partial Loss}{\\partial o_1} \\cdot \\frac{\\partial o_1}{\\partial y_1} +\n",
    "            \\frac{\\partial Loss}{\\partial o_2} \\cdot \\frac{\\partial o_2}{\\partial y_1} \n",
    "       \\right) \\cdot \\frac{\\partial y_1}{\\partial w_{11}^{(2)}} \\nonumber \\\\\n",
    "    &= (o_2^* \\cdot o_1 - o_1^* \\cdot o_2) \\cdot a_1 \\nonumber\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed479e8a",
   "metadata": {},
   "source": [
    "## 更新权重\n",
    "\n",
    "$w_{11}^{(2)}(new) = w_{11}^{(2)}(old) - learning_{rate} \\cdot \\frac{\\partial Loss}{\\partial w_{11}^{(2)}} $\n",
    "\n",
    "### SGD Optimizer\n",
    "$w_{t+1} = w_t - \\alpha \\cdot g(w_t)$\n",
    "\n",
    "缺点：\n",
    "\n",
    "    1. 易受样本噪声影响\n",
    "    2. 可能陷入局部最优解\n",
    "    \n",
    "### SGD+Momentum\n",
    "$ v_t = \\eta \\cdot v_{t-1} + \\alpha \\cdot g(w_t)$\n",
    "\n",
    "$w_{t+1} = w_t - v_t$\n",
    "\n",
    "### Adagrad Optimizer\n",
    "$ s_t = s_{t-1} + g^2(w_t) $\n",
    "\n",
    "$w_{t+1} = w_t - \\frac{\\alpha}{\\sqrt{s_t + \\epsilon}} \\cdot g(w_t)$\n",
    "\n",
    "缺点： 学习率下降太快，可能还没收敛就停止训练了\n",
    "\n",
    "### RMSProp Optimizer\n",
    "$ s_t = \\eta \\cdot s_{t-1} + (1-\\eta) \\cdot g^2(w_t) $\n",
    "\n",
    "$w_{t+1} = w_t - \\frac{\\alpha}{\\sqrt{s_t + \\epsilon}} \\cdot g(w_t)$\n",
    "\n",
    "### Adam Optimizer\n",
    "$ m_t = \\beta_1 \\cdot m_{t-1} + (1-\\beta_1) \\cdot g(w_t)$  一阶动量\n",
    "\n",
    "$ v_t = \\beta_2 \\cdot v_{t-1} + (1-\\beta_2) \\cdot g^2(w_t)$ 二阶动量\n",
    "\n",
    "$\\hat{m}_t = \\frac{m_t}{1-\\beta_1^t}$\n",
    "\n",
    "$\\hat{v}_t = \\frac{v_t}{1-\\beta_2^t}$\n",
    "\n",
    "$w_{t+1} = w_t - \\frac{\\alpha}{\\sqrt{\\hat{v}_t+\\epsilon}} \\cdot \\hat{m}_t$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4caf286",
   "metadata": {},
   "source": [
    "# LeNet Example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bbf1a1a",
   "metadata": {},
   "source": [
    "## Load and normalize CIFAR10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d85421cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-10T09:52:29.810095Z",
     "start_time": "2022-04-10T09:52:06.246556Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "67116f16",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-10T09:52:29.825333Z",
     "start_time": "2022-04-10T09:52:29.813185Z"
    }
   },
   "outputs": [],
   "source": [
    "# 设置transform 对图像进行预处理\n",
    "transform = transforms.Compose(\n",
    "    [\n",
    "        # 将Pic转换为Tensor 并转化到[0,1]\n",
    "        transforms.ToTensor(),\n",
    "        # 对输入标准化 均值0.5 标准差0.5\n",
    "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "810962c0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-10T09:52:33.247427Z",
     "start_time": "2022-04-10T09:52:29.827310Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "batch_size = 4  # 分批大小\n",
    "\n",
    "# 训练数据集\n",
    "trainset = torchvision.datasets.CIFAR10(\n",
    "    root='./data',  # 数据根目录\n",
    "    train=True,  # 是训练集\n",
    "    download=True,  # 自动下载\n",
    "    transform=transform  # 预处理方法\n",
    ")\n",
    "\n",
    "# 训练数据加载器\n",
    "trainloader = torch.utils.data.DataLoader(\n",
    "    trainset,  # 设置数据集\n",
    "    batch_size=batch_size,  # 分批大小\n",
    "    shuffle=True,  # 打乱顺序 是\n",
    "    num_workers=2  # 2线程工作\n",
    ")\n",
    "\n",
    "# 测试数据集\n",
    "testset = torchvision.datasets.CIFAR10(\n",
    "    root='./data',\n",
    "    train=False,  # 是测试集\n",
    "    download=True,\n",
    "    transform=transform\n",
    ")\n",
    "\n",
    "# 测试数据加载器\n",
    "testloader = torch.utils.data.DataLoader(\n",
    "    testset,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=False,  # 打乱顺序 否\n",
    "    num_workers=2\n",
    ")\n",
    "\n",
    "classes = (\n",
    "    'plane', 'car', 'bird', 'cat', 'deer',\n",
    "    'dog', 'frog', 'horse', 'ship', 'truck'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f09fe86",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-10T09:52:38.046080Z",
     "start_time": "2022-04-10T09:52:33.250411Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deer  car   horse dog  \n"
     ]
    }
   ],
   "source": [
    "# 查看一些图片\n",
    "\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5  # 去标准化\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# 随机抽取图片\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "\n",
    "print(' '.join(f'{classes[labels[j]]:5s}' for j in range(batch_size)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8796125b",
   "metadata": {},
   "source": [
    "## Define a Convolutional Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "649e82d8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-10T09:52:38.062137Z",
     "start_time": "2022-04-10T09:52:38.048318Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6dd78d2b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-10T09:52:38.077937Z",
     "start_time": "2022-04-10T09:52:38.064191Z"
    }
   },
   "outputs": [],
   "source": [
    "class LeNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        # in (3,32,32)\n",
    "        self.conv1 = nn.Conv2d(3, 16, 5)  # out (16,28,28)\n",
    "        self.pool1 = nn.MaxPool2d(2, 2)  # (16,14,14)\n",
    "        self.conv2 = nn.Conv2d(16, 32, 5)  # out (32,10,10)\n",
    "        self.pool2 = nn.MaxPool2d(2, 2)  # out (32,5,5)\n",
    "\n",
    "        self.fc1 = nn.Linear(32*5*5, 120)\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.conv1(x))  # 第一层卷积\n",
    "        x = self.pool1(x)  # 池化下采样\n",
    "        x = F.relu(self.conv2(x))  # 第二层卷积\n",
    "        x = self.pool2(x)  # 池化下采样\n",
    "        # x = x.view(-1, 32*5*5)\n",
    "        x = torch.flatten(x, 1)  # torch的新展平方法 从dim = 1开始展平\n",
    "        x = F.relu(self.fc1(x))  # 全连接1\n",
    "        x = F.relu(self.fc2(x))  # 全连接2\n",
    "        x = self.fc3(x)  # 全连接3 输出 输出不需要softmax 交叉熵自带了softmax\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8aaa79c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-10T09:52:39.412879Z",
     "start_time": "2022-04-10T09:52:38.080910Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainning Device: cuda:0\n"
     ]
    }
   ],
   "source": [
    "# 确定训练设备\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available else 'cpu')\n",
    "net = LeNet()\n",
    "print(f'Trainning Device: {device}')\n",
    "# 网络迁移到训练设备上\n",
    "net.to(device)\n",
    "# 使用交叉熵损失\n",
    "loss_function = nn.CrossEntropyLoss()\n",
    "# 使用Adam优化器 学习率设置0.001\n",
    "optimizer = optim.Adam(net.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea731f08",
   "metadata": {},
   "source": [
    "## Train this network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7078ffbb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-10T09:59:31.925012Z",
     "start_time": "2022-04-10T09:52:39.414890Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[1/5]\n",
      "[1250/12500] running_loss: 2.0073\n",
      "[2500/12500] running_loss: 1.8509\n",
      "[3750/12500] running_loss: 1.7636\n",
      "[5000/12500] running_loss: 1.7096\n",
      "[6250/12500] running_loss: 1.672\n",
      "[7500/12500] running_loss: 1.632\n",
      "[8750/12500] running_loss: 1.6012\n",
      "[10000/12500] running_loss: 1.5753\n",
      "[11250/12500] running_loss: 1.5513\n",
      "[12500/12500] running_loss: 1.5294\n",
      "Train loss: 1.5293, Test accuracy: 53.400%\n",
      "Epoch[2/5]\n",
      "[1250/12500] running_loss: 1.2979\n",
      "[2500/12500] running_loss: 1.2847\n",
      "[3750/12500] running_loss: 1.281\n",
      "[5000/12500] running_loss: 1.276\n",
      "[6250/12500] running_loss: 1.2779\n",
      "[7500/12500] running_loss: 1.2687\n",
      "[8750/12500] running_loss: 1.2647\n",
      "[10000/12500] running_loss: 1.2593\n",
      "[11250/12500] running_loss: 1.252\n",
      "[12500/12500] running_loss: 1.245\n",
      "Train loss: 1.2449, Test accuracy: 56.550%\n",
      "Epoch[3/5]\n",
      "[1250/12500] running_loss: 1.1682\n",
      "[2500/12500] running_loss: 1.1449\n",
      "[3750/12500] running_loss: 1.1451\n",
      "[5000/12500] running_loss: 1.143\n",
      "[6250/12500] running_loss: 1.1407\n",
      "[7500/12500] running_loss: 1.1381\n",
      "[8750/12500] running_loss: 1.1399\n",
      "[10000/12500] running_loss: 1.1394\n",
      "[11250/12500] running_loss: 1.1363\n",
      "[12500/12500] running_loss: 1.1346\n",
      "Train loss: 1.1346, Test accuracy: 59.350%\n",
      "Epoch[4/5]\n",
      "[1250/12500] running_loss: 1.0207\n",
      "[2500/12500] running_loss: 1.0428\n",
      "[3750/12500] running_loss: 1.0406\n",
      "[5000/12500] running_loss: 1.0447\n",
      "[6250/12500] running_loss: 1.0486\n",
      "[7500/12500] running_loss: 1.0544\n",
      "[8750/12500] running_loss: 1.0555\n",
      "[10000/12500] running_loss: 1.0591\n",
      "[11250/12500] running_loss: 1.0586\n",
      "[12500/12500] running_loss: 1.0594\n",
      "Train loss: 1.0593, Test accuracy: 60.740%\n",
      "Epoch[5/5]\n",
      "[1250/12500] running_loss: 0.98016\n",
      "[2500/12500] running_loss: 0.97673\n",
      "[3750/12500] running_loss: 0.9848\n",
      "[5000/12500] running_loss: 0.98981\n",
      "[6250/12500] running_loss: 0.99282\n",
      "[7500/12500] running_loss: 0.99632\n",
      "[8750/12500] running_loss: 0.99612\n",
      "[10000/12500] running_loss: 0.9966\n",
      "[11250/12500] running_loss: 1.0016\n",
      "[12500/12500] running_loss: 1.001\n",
      "Train loss: 1.0009, Test accuracy: 63.280%\n"
     ]
    }
   ],
   "source": [
    "batches = int(len(trainset) / batch_size)  # 总的batch数目\n",
    "epoches = 5  # epoch 数目\n",
    "\n",
    "# 一个epoch的训练函数\n",
    "\n",
    "\n",
    "def train(trainloader):\n",
    "    running_loss = 0.0  # 总体训练损失\n",
    "    for step, data in enumerate(trainloader, 0):\n",
    "        # 从迭代器取数据 迁移到训练设备\n",
    "        inputs, labels = data[0].to(device), data[1].to(device)\n",
    "        # 清空梯度 否则梯度会累积\n",
    "        optimizer.zero_grad()\n",
    "        # 正向传播\n",
    "        outputs = net(inputs)\n",
    "        # 计算损失\n",
    "        loss = loss_function(outputs, labels)\n",
    "        # 反向传播 计算梯度\n",
    "        loss.backward()\n",
    "        # 优化器优化 更新参数\n",
    "        optimizer.step()\n",
    "        # 累加损失\n",
    "        running_loss += loss.item()\n",
    "        # 每1250步打印损失\n",
    "        if step % 1250 == 1249:\n",
    "            print(f'[{step + 1}/{batches}] running_loss: {(running_loss/step):5.5}')\n",
    "    # 返回本个epoch的最终训练损失\n",
    "    return running_loss / batches\n",
    "\n",
    "# 测试函数\n",
    "\n",
    "\n",
    "def test(testloader):\n",
    "    test_acc = 0.0\n",
    "    cnt = 0\n",
    "    for _, data in enumerate(testloader, 0):\n",
    "        # 关闭自动梯度\n",
    "        with torch.no_grad():\n",
    "            inputs, labels = data[0].to(device), data[1].to(device)\n",
    "            outputs = net(inputs)\n",
    "            # 使用测试集数据计算准确率\n",
    "            pred = torch.max(outputs, dim=1)[1]\n",
    "            acc = (pred == labels).sum().item() / labels.size(0)\n",
    "            test_acc = test_acc + acc\n",
    "            cnt = cnt + 1\n",
    "    test_acc = test_acc / cnt\n",
    "    # 返回测试集上的准确率\n",
    "    return test_acc\n",
    "\n",
    "\n",
    "# 训练过程\n",
    "for epoch in range(epoches):\n",
    "    print(f'Epoch[{epoch+1}/{epoches}]')\n",
    "    train_loss = train(trainloader)\n",
    "    test_acc = test(testloader)\n",
    "    print(f'Train loss: {train_loss:5.5}, Test accuracy: {test_acc:5.3%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4107bf6d",
   "metadata": {},
   "source": [
    "## Save weight to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6841c5b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-10T09:59:32.163181Z",
     "start_time": "2022-04-10T09:59:31.927022Z"
    }
   },
   "outputs": [],
   "source": [
    "save_path = r'./lenet.pth'\n",
    "torch.save(net.state_dict(), save_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d452fc51",
   "metadata": {},
   "source": [
    "## Test Network on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9aa47e09",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-10T09:59:32.763996Z",
     "start_time": "2022-04-10T09:59:32.166408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "\n",
    "pred_net = LeNet()\n",
    "pred_net.load_state_dict(torch.load(save_path))\n",
    "pred_net.to(device)\n",
    "\n",
    "im = Image.open(r'1.jpg')\n",
    "im_r = im.resize((32, 32))\n",
    "im_v = transform(im_r)\n",
    "im_v = torch.unsqueeze(im_v, dim=0)\n",
    "im_v = im_v.to(device)\n",
    "\n",
    "with torch.no_grad():\n",
    "    output = pred_net(im_v)\n",
    "    predict = torch.softmax(output, dim=1)\n",
    "    label = classes[predict[0].argmax().item()]\n",
    "    plt.imshow(im)\n",
    "    plt.text(im.size[0]/2, -im.size[1]/50, label, ha='center', fontsize=12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14d1f5e8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "330px",
    "width": "227px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
